Imaging devices such as Magnetic Resonance Imaging (MRI) scanners provide myriad options which refine image acquisition or processing procedures based on feedback from the user. In order to provide meaningful feedback, the user must be able to articulate his or her concerns in terms of image quality. However, image quality is a fuzzy notion. It varies with the modality, the use-case, and the customer. There are several steps in the imaging and rendering chain which have parameters that can be tuned to affect image quality, but finding the right tuning requires solving two challenging problems. First, since there is not an established description standard for image quality, it is challenging for users to comment upon, or suggest modifications to, image data or its related parameters. Secondly, even when the customer requirements are known, determining the optimal parameter settings may be difficult. For example, a particular change to an image characteristic may require changes to several, unknown parameters.
Moreover, the act of getting feedback on image quality is very much of an art. For example, often a service engineer must visit the customer site and proceed by trial and error: dialoging with the customer to try to understand their problem, tuning the parameters, showing the result to the customer, and iterating until the customer is satisfied with the result. This process is time intensive and wastes resources. Accordingly, it is desired to create a technique for gathering image feedback which may be automated, but still personalized, to address the issues set out above.